




"""simple docstring"""






from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap





def 				UpperCAmelCase		(		snake_case      :       Union[str, Any]     ):
					return getitem, k





def 				UpperCAmelCase		(		snake_case      :       int   ,							snake_case      :       Dict     ):
					return setitem, k, v





def 				UpperCAmelCase		(		snake_case      :       Optional[Any]     ):
					return delitem, k





def 				UpperCAmelCase		(		snake_case      :       Optional[int]   ,							snake_case      :       Dict   ,							*snake_case      :       Tuple     ):
					try:
										return fun(a_   ,							*a_     ), None
					except Exception as e:
										return None, e


UpperCamelCase__    =      (
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
)

UpperCamelCase__    =      [
    _set('''key_a''', '''val_a'''),
    _set('''key_a''', '''val_b'''),
]

UpperCamelCase__    =      [
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
    _del('''key_a'''),
    _del('''key_b'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
]

UpperCamelCase__    =      [
    _get('''key_a'''),
    _del('''key_a'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
    _del('''key_a'''),
    _get('''key_a'''),
]

UpperCamelCase__    =      [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

UpperCamelCase__    =      [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('''key_a''', '''val_b'''),
]





@pytest.mark.parametrize(
    '''operations'''   ,							(
        pytest.param(_add_items   ,							id='''add items'''     ),
        pytest.param(_overwrite_items   ,							id='''overwrite items'''     ),
        pytest.param(_delete_items   ,							id='''delete items'''     ),
        pytest.param(_access_absent_items   ,							id='''access absent items'''     ),
        pytest.param(_add_with_resize_up   ,							id='''add with resize up'''     ),
        pytest.param(_add_with_resize_down   ,							id='''add with resize down'''     ),
    )   ,							)
def 				UpperCAmelCase		(		snake_case      :       List[str]     ):
					_lowerCAmelCase:int						=	HashMap(initial_block_size=4     )
					_lowerCAmelCase:Any						=	{}
					for _, (fun, *args) in enumerate(a_     ):
										_lowerCAmelCase:Any						=	_run_operation(a_   ,							a_   ,							*a_     )
										_lowerCAmelCase:int						=	_run_operation(a_   ,							a_   ,							*a_     )
										assert my_res == py_res
										assert str(a_     ) == str(a_     )
										assert set(a_     ) == set(a_     )
										assert len(a_     ) == len(a_     )
										assert set(my.items()     ) == set(py.items()     )





def 				UpperCAmelCase		(		):
					def is_public(snake_case      :       str     ) -> bool:
										return not name.startswith('''_'''     )

					_lowerCAmelCase:List[Any]						=	{name for name in dir({}     ) if is_public(a_     )}
					_lowerCAmelCase:Any						=	{name for name in dir(HashMap()     ) if is_public(a_     )}

					assert dict_public_names > hash_public_names













'''simple docstring'''






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def   __A    (				a_    :	int   ,a_    :	str=0.9_9_9   ,a_    :	List[str]="cosine"   ,):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(a_    :	List[Any]						):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(a_    :	List[str]						):
						return math.exp(t * -1_2.0						)

		else:
				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

		lowerCAmelCase  :     Optional[int]       						= []
		for i in range(a_						):
				lowerCAmelCase  :     Dict       						= i / num_diffusion_timesteps
				lowerCAmelCase  :     List[Any]       						= (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(a_						) / alpha_bar_fn(a_						)   ,a_						)						)
		return torch.tensor(a_   ,dtype=torch.floataa						)

class 		lowerCamelCase	(   _A   , _A							):
	snake_case_   = [e.name for e in KarrasDiffusionSchedulers]
	snake_case_   = 2



	@register_to_config
	def __init__( self		,   a_ = 1_000		,   a_ = 0.00085		,   a_ = 0.012		,   a_ = "linear"		,   a_ = None		,   a_ = "epsilon"		,   a_ = False		,   a_ = False		,   a_ = 1.0		,   a_ = "linspace"		,   a_ = 0		,   ):
			if trained_betas is not None:
					lowerCAmelCase  :     List[Any]       						= torch.tensor(a_		,   dtype=torch.floataa						)
			elif beta_schedule == "linear":
					lowerCAmelCase  :     List[str]       						= torch.linspace(a_		,   a_		,   a_		,   dtype=torch.floataa						)
			elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					lowerCAmelCase  :     List[str]       						= (
					    torch.linspace(beta_start**0.5		,   beta_end**0.5		,   a_		,   dtype=torch.floataa						) ** 2
					)
			elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					lowerCAmelCase  :     List[Any]       						= betas_for_alpha_bar(a_		,   alpha_transform_type="cosine"						)
			elif beta_schedule == "exp":
					lowerCAmelCase  :     Any       						= betas_for_alpha_bar(a_		,   alpha_transform_type="exp"						)
			else:
					raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''						)

			lowerCAmelCase  :     Optional[int]       						= 1.0 - self.betas
			lowerCAmelCase  :     Optional[Any]       						= torch.cumprod(self.alphas		,   dim=0						)

			#  set all values
			self.set_timesteps(a_		,   a_		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= use_karras_sigmas



	def 	_lowerCamelCase     ( self		,   a_		,   a_=None						):
			if schedule_timesteps is None:
					lowerCAmelCase  :     List[str]       						= self.timesteps

			lowerCAmelCase  :     int       						= (schedule_timesteps == timestep).nonzero()

			# The sigma index that is taken for the **very** first `step`
			# is always the second index (or the last index if there is only 1)
			# This way we can ensure we don't accidentally skip a sigma in
			# case we start in the middle of the denoising schedule (e.g. for image-to-image)
			if len(self._index_counter						) == 0:
					lowerCAmelCase  :     Union[str, Any]       						= 1 if len(a_						) > 1 else 0
			else:
					lowerCAmelCase  :     Tuple       						= timestep.cpu().item() if torch.is_tensor(a_						) else timestep
					lowerCAmelCase  :     Any       						= self._index_counter[timestep_int]

			return indices[pos].item()



	@property
	def 	_lowerCamelCase     ( self						):
			# standard deviation of the initial noise distribution
			if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

			return (self.sigmas.max() ** 2 + 1) ** 0.5



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   ):
			lowerCAmelCase  :     Union[str, Any]       						= self.index_for_timestep(a_						)

			lowerCAmelCase  :     Any       						= self.sigmas[step_index]
			lowerCAmelCase  :     int       						= sample / ((sigma**2 + 1) ** 0.5)
			return sample



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   ):
			lowerCAmelCase  :     List[Any]       						= num_inference_steps

			lowerCAmelCase  :     Optional[int]       						= num_train_timesteps or self.config.num_train_timesteps

			# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
			if self.config.timestep_spacing == "linspace":
					lowerCAmelCase  :     Optional[Any]       						= np.linspace(0		,   num_train_timesteps - 1		,   a_		,   dtype=a_						)[::-1].copy()
			elif self.config.timestep_spacing == "leading":
					lowerCAmelCase  :     str       						= num_train_timesteps // self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					lowerCAmelCase  :     Dict       						= (np.arange(0		,   a_						) * step_ratio).round()[::-1].copy().astype(a_						)
					timesteps += self.config.steps_offset
			elif self.config.timestep_spacing == "trailing":
					lowerCAmelCase  :     Tuple       						= num_train_timesteps / self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					lowerCAmelCase  :     int       						= (np.arange(a_		,   0		,   -step_ratio						)).round().copy().astype(a_						)
					timesteps -= 1
			else:
					raise ValueError(
					    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''						)

			lowerCAmelCase  :     Any       						= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
			lowerCAmelCase  :     Any       						= np.log(a_						)
			lowerCAmelCase  :     int       						= np.interp(a_		,   np.arange(0		,   len(a_						)						)		,   a_						)

			if self.config.use_karras_sigmas:
					lowerCAmelCase  :     int       						= self._convert_to_karras(in_sigmas=a_		,   num_inference_steps=self.num_inference_steps						)
					lowerCAmelCase  :     Any       						= np.array([self._sigma_to_t(a_		,   a_						) for sigma in sigmas]						)

			lowerCAmelCase  :     int       						= np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
			lowerCAmelCase  :     int       						= torch.from_numpy(a_						).to(device=a_						)
			lowerCAmelCase  :     Any       						= torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2						), sigmas[-1:]]						)

			lowerCAmelCase  :     Union[str, Any]       						= torch.from_numpy(a_						)
			lowerCAmelCase  :     Union[str, Any]       						= torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2						)]						)

			if str(a_						).startswith("mps"						):
					# mps does not support float64
					lowerCAmelCase  :     str       						= timesteps.to(a_		,   dtype=torch.floataa						)
			else:
					lowerCAmelCase  :     int       						= timesteps.to(device=a_						)

			# empty dt and derivative
			lowerCAmelCase  :     Union[str, Any]       						= None
			lowerCAmelCase  :     Optional[Any]       						= None

			# for exp beta schedules, such as the one for `pipeline_shap_e.py`
			# we need an index counter
			lowerCAmelCase  :     int       						= defaultdict(a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			# get log sigma
			lowerCAmelCase  :     str       						= np.log(a_						)

			# get distribution
			lowerCAmelCase  :     int       						= log_sigma - log_sigmas[:, np.newaxis]

			# get sigmas range
			lowerCAmelCase  :     int       						= np.cumsum((dists >= 0)		,   axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
			lowerCAmelCase  :     Any       						= low_idx + 1

			lowerCAmelCase  :     str       						= log_sigmas[low_idx]
			lowerCAmelCase  :     Optional[Any]       						= log_sigmas[high_idx]

			# interpolate sigmas
			lowerCAmelCase  :     Optional[Any]       						= (low - log_sigma) / (low - high)
			lowerCAmelCase  :     List[str]       						= np.clip(a_		,   0		,   1						)

			# transform interpolation to time range
			lowerCAmelCase  :     int       						= (1 - w) * low_idx + w * high_idx
			lowerCAmelCase  :     Dict       						= t.reshape(sigma.shape						)
			return t



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			lowerCAmelCase  :     float       						= in_sigmas[-1].item()
			lowerCAmelCase  :     float       						= in_sigmas[0].item()

			lowerCAmelCase  :     str       						= 7.0  # 7.0 is the value used in the paper
			lowerCAmelCase  :     Optional[int]       						= np.linspace(0		,   1		,   a_						)
			lowerCAmelCase  :     Optional[int]       						= sigma_min ** (1 / rho)
			lowerCAmelCase  :     str       						= sigma_max ** (1 / rho)
			lowerCAmelCase  :     List[str]       						= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
			return sigmas



	@property
	def 	_lowerCamelCase     ( self						):
			return self.dt is None



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_ = True		,   ):
			lowerCAmelCase  :     Optional[int]       						= self.index_for_timestep(a_						)

			# advance index counter by 1
			lowerCAmelCase  :     List[str]       						= timestep.cpu().item() if torch.is_tensor(a_						) else timestep
			self._index_counter[timestep_int] += 1

			if self.state_in_first_order:
					lowerCAmelCase  :     Optional[Any]       						= self.sigmas[step_index]
					lowerCAmelCase  :     Dict       						= self.sigmas[step_index + 1]
			else:
					# 2nd order / Heun's method
					lowerCAmelCase  :     List[str]       						= self.sigmas[step_index - 1]
					lowerCAmelCase  :     int       						= self.sigmas[step_index]

			# currently only gamma=0 is supported. This usually works best anyways.
			# We can support gamma in the future but then need to scale the timestep before
			# passing it to the model which requires a change in API
			lowerCAmelCase  :     int       						= 0
			lowerCAmelCase  :     Optional[int]       						= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

			# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
			if self.config.prediction_type == "epsilon":
					lowerCAmelCase  :     int       						= sigma_hat if self.state_in_first_order else sigma_next
					lowerCAmelCase  :     Tuple       						= sample - sigma_input * model_output
			elif self.config.prediction_type == "v_prediction":
					lowerCAmelCase  :     List[str]       						= sigma_hat if self.state_in_first_order else sigma_next
					lowerCAmelCase  :     int       						= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
					    sample / (sigma_input**2 + 1)
					)
			elif self.config.prediction_type == "sample":
					lowerCAmelCase  :     Optional[int]       						= model_output
			else:
					raise ValueError(
					    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

			if self.config.clip_sample:
					lowerCAmelCase  :     Optional[int]       						= pred_original_sample.clamp(
					    -self.config.clip_sample_range		,   self.config.clip_sample_range						)

			if self.state_in_first_order:
					# 2. Convert to an ODE derivative for 1st order
					lowerCAmelCase  :     Union[str, Any]       						= (sample - pred_original_sample) / sigma_hat
					# 3. delta timestep
					lowerCAmelCase  :     Optional[int]       						= sigma_next - sigma_hat

					# store for 2nd order step
					lowerCAmelCase  :     List[str]       						= derivative
					lowerCAmelCase  :     Optional[int]       						= dt
					lowerCAmelCase  :     Dict       						= sample
			else:
					# 2. 2nd order / Heun's method
					lowerCAmelCase  :     Dict       						= (sample - pred_original_sample) / sigma_next
					lowerCAmelCase  :     Optional[int]       						= (self.prev_derivative + derivative) / 2

					# 3. take prev timestep & sample
					lowerCAmelCase  :     Optional[int]       						= self.dt
					lowerCAmelCase  :     Tuple       						= self.sample

					# free dt and derivative
					# Note, this puts the scheduler in "first order mode"
					lowerCAmelCase  :     Tuple       						= None
					lowerCAmelCase  :     Optional[Any]       						= None
					lowerCAmelCase  :     Union[str, Any]       						= None

			lowerCAmelCase  :     Any       						= sample + derivative * dt

			if not return_dict:
					return (prev_sample,)

			return SchedulerOutput(prev_sample=a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   ):
			# Make sure sigmas and timesteps have the same device and dtype as original_samples
			lowerCAmelCase  :     Tuple       						= self.sigmas.to(device=original_samples.device		,   dtype=original_samples.dtype						)
			if original_samples.device.type == "mps" and torch.is_floating_point(a_						):
					# mps does not support float64
					lowerCAmelCase  :     Optional[int]       						= self.timesteps.to(original_samples.device		,   dtype=torch.floataa						)
					lowerCAmelCase  :     Dict       						= timesteps.to(original_samples.device		,   dtype=torch.floataa						)
			else:
					lowerCAmelCase  :     List[Any]       						= self.timesteps.to(original_samples.device						)
					lowerCAmelCase  :     str       						= timesteps.to(original_samples.device						)

			lowerCAmelCase  :     Tuple       						= [self.index_for_timestep(a_		,   a_						) for t in timesteps]

			lowerCAmelCase  :     Dict       						= sigmas[step_indices].flatten()
			while len(sigma.shape						) < len(original_samples.shape						):
					lowerCAmelCase  :     Any       						= sigma.unsqueeze(-1						)

			lowerCAmelCase  :     str       						= original_samples + noise * sigma
			return noisy_samples





	def __len__( self						):
			return self.config.num_train_timesteps



